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In this article, we derive statistical tools to analyze and distinguish the patterns of genetic variability produced by classical and recent population genetic models related to seed banks. In particular, we are concerned with models…

Populations and Evolution · Quantitative Biology 2020-01-10 Jochen Blath , Eugenio Buzzoni , Jere Koskela , Maite Wilke Berenguer

We study a model of selection acting on a diploid population (one in which each individual carries two copies of each gene) living in one spatial dimension. We suppose a particular gene appears in two forms (alleles) $A$ and $a$, and that…

Probability · Mathematics 2020-09-09 Alison Etheridge , Sarah Penington

We consider an expanding population on the plane. The genealogy of a sample from the population is modelled by coalescing Brownian motion on the circle. We establish a weak law of large numbers for the site frequency spectrum in this model.…

Probability · Mathematics 2023-08-16 Yubo Shuai

The coalescent is a foundational model of latent genealogical trees under neutral evolution, but suffers from intractable sampling probabilities. Methods for approximating these sampling probabilities either introduce bias or fail to scale…

Statistics Theory · Mathematics 2026-02-19 Martina Favero , Jere Koskela

We study the multi-strategy stochastic evolutionary game with death-birth updating in expanding spatial populations of size $N\to \infty$. The model is a voter model perturbation. For typical populations, we require perturbation strengths…

Probability · Mathematics 2021-01-13 Yu-Ting Chen

In this work we describe a new model for the evolution of a diploid structured population backwards in time that allows for large migrations and uneven offspring distributions. The model generalizes both the mean-field model of Birkner et…

Probability · Mathematics 2026-02-11 Maximillian Newman

We dedicate this paper to Sir John Kingman on his 70th Birthday. In modern mathematical population genetics the ancestral history of a population of genes back in time is described by John Kingman's coalescent tree. Classical and modern…

Probability · Mathematics 2010-03-25 Robert C. Griffiths , Dario Spano`

The coalescent is a stochastic process representing ancestral lineages in a population undergoing neutral genetic drift. Originally defined for a well-mixed population, the coalescent has been adapted in various ways to accommodate spatial,…

Populations and Evolution · Quantitative Biology 2024-07-02 Benjamin Allen , Alex McAvoy

We study the multi-type Cannings population model. Each individual has a type belonging to a given at most countable type space $E$. The population is hence divided into $|E|$ subpopulations. The subpopulation sizes are assumed to be…

Probability · Mathematics 2025-04-16 Maximilian Flamm , Martin Möhle

We establish convergence to the Kingman coalescent for a class of age-structured population models with time-constant population size. Time is discrete with unit called a year. Offspring numbers in a year may depend on mother's age.

Probability · Mathematics 2007-05-23 Serik Sagitov , Peter Jagers

We derive the asymptotic distribution of the total length $L_n$ of a $\operatorname {Beta}(2-\alpha,\alpha)$-coalescent tree for $1<\alpha<2$, starting from $n$ individuals. There are two regimes: If $\alpha\le1/2(1+\sqrt{5})$, then $L_n$…

Probability · Mathematics 2012-10-22 Götz Kersting

The problem of estimating the growth rate of a birth and death processes based on the coalescence times of a sample of $n$ individuals has been considered by several authors (\cite{stadler2009incomplete, williams2022life,…

Methodology · Statistics 2026-02-26 Carola Sophia Heinzel , Jason Schweinsberg

The evolving Kingman coalescent is the tree-valued process which records the time evolution undergone by the genealogies of Moran populations. We consider the associated process of total external tree length of the evolving Kingman…

Probability · Mathematics 2016-06-20 Iulia Dahmer , Götz Kersting

Recent progress has been made in establishing normal approximation bounds in terms of the Wasserstein-$p$ distance for i.i.d. and locally dependent random variables. However, for $p > 1$, no such results have been demonstrated for dependent…

Probability · Mathematics 2025-02-25 Tianle Liu , Morgane Austern

This paper extends earlier work by Cox and Durrett, who studied the coalescence times for two lineages in the stepping stone model on the two-dimensional torus. We show that the genealogy of a sample of size n is given by a time change of…

Probability · Mathematics 2007-05-23 Iljana Zahle , J. Theodore Cox , Richard Durrett

We introduce a new Wright-Fisher type model for seed banks incorporating "simultaneous switching", which is motivated by recent work on microbial dormancy. We show that the simultaneous switching mechanism leads to a new jump-diffusion…

Populations and Evolution · Quantitative Biology 2018-12-24 Jochen Blath , Adrián González Casanova , Noemi Kurt , Maite Wilke-Berenguer

We consider a family of models describing the evolution under selection of a population whose dynamics can be related to the propagation of noisy traveling waves. For one particular model, that we shall call the exponential model, the…

Disordered Systems and Neural Networks · Physics 2009-11-13 E. Brunet , B. Derrida , A. H. Mueller , S. Munier

This paper studies the bounded confidence model on growing fully-mixed populations. In this model, in addition to the usual opinion clusters, significant secondary clusters of smaller size appear systematically, while those secondary…

Physics and Society · Physics 2024-02-20 Yérali Gandica , Guillaume Deffuant

The Beta coalescents are stochastic processes modeling the genealogy of a population. They appear as the rescaled limits of the genealogical trees of numerous stochastic population models. In this article, we take interest in the number of…

Probability · Mathematics 2019-05-21 Yier Lin , Bastien Mallein

We introduce a new Bayesian model for hierarchical clustering based on a prior over trees called Kingman's coalescent. We develop novel greedy and sequential Monte Carlo inferences which operate in a bottom-up agglomerative fashion. We show…

Machine Learning · Statistics 2009-07-07 Yee Whye Teh , Hal Daumé , Daniel Roy
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